# search.py
# ---------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html

"""
In search.py, you will implement generic search algorithms which are called
by Pacman agents (in searchAgents.py).
"""

import util

class Node:
    
    def  __init__(self, current, actions=[], pathCost=0):
        self.currentState = current
        self.actions = actions
        self.pathCost = pathCost
        
    def getCurrentState(self):
        return self.currentState
    
    def getActions(self):
        return self.actions
    
    def getPathCost(self):
        return self.pathCost
    
    #not sure if need setters but here they are for now
    def setCurrentState(self, state):
        self.currentState = state
        
    def setActions(self, actions):
        self.actions= actions
    
    def setPathCost(self, cost):
        self.pathCost = cost

class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).

    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state

        Returns True if and only if the state is a valid goal state
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state

        For a given state, this should return a list of triples,
        (successor, action, stepCost), where 'successor' is a
        successor to the current state, 'action' is the action
        required to get there, and 'stepCost' is the incremental
        cost of expanding to that successor
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take

        This method returns the total cost of a particular sequence of actions.  The sequence must
        be composed of legal moves
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other
    maze, the sequence of moves will be incorrect, so only use this for tinyMaze
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return  [s,s,w,s,w,w,s,w]

def depthFirstSearch(problem):
    """
    Search the deepest nodes in the search tree first
    [2nd Edition: p 75, 3rd Edition: p 87]

    Your search algorithm needs to return a list of actions that reaches
    the goal.  Make sure to implement a graph search algorithm
    [2nd Edition: Fig. 3.18, 3rd Edition: Fig 3.7].

    To get started, you might want to try some of these simple commands to
    understand the search problem that is being passed in:

    print "Start:", problem.getStartState()
    print "Is the start a goal?", problem.isGoalState(problem.getStartState())
    print "Start's successors:", problem.getSuccessors(problem.getStartState())
    """
    "*** YOUR CODE HERE ***"
    
    fringe = util.Stack()
    fringe.push(Node(problem.getStartState()))
    
    explored = set()
    explored.add(problem.getStartState())
    
    while not fringe.isEmpty():
        node = fringe.pop()
        successors = problem.getSuccessors(node.getCurrentState())
        
        for triple in successors:
            newActions = list(node.getActions())
            newActions.append(triple[1])
            
            child = Node(triple[0], newActions, (node.getPathCost() + triple[2]))
            
            if child.getCurrentState() not in explored: 
                if problem.isGoalState(child.getCurrentState()):
                    return child.getActions()            
                fringe.push(child)
                explored.add(child.getCurrentState())

def breadthFirstSearch(problem):
    """
    Search the shallowest nodes in the search tree first.
    [2nd Edition: p 73, 3rd Edition: p 82]
    """
    fringe = util.Queue()
    fringe.push(Node(problem.getStartState()))
    
    explored = set()
    explored.add(problem.getStartState())
    
    while not fringe.isEmpty():
        node = fringe.pop()
        successors = problem.getSuccessors(node.getCurrentState())
        
        for triple in successors:
            newActions = list(node.getActions())
            newActions.append(triple[1])
            
            child = Node(triple[0], newActions, (node.getPathCost() + triple[2]))
            
            if child.getCurrentState() not in explored: 
                if problem.isGoalState(child.getCurrentState()):
                    return child.getActions()            
                fringe.push(child)
                explored.add(child.getCurrentState())

def uniformCostSearch(problem):
    "Search the node of least total cost first. "
    "*** YOUR CODE HERE ***"
    
    fringe = util.PriorityQueue()
    fringe.push(Node(problem.getStartState()), 0)
    
    explored = set()
    
    while not fringe.isEmpty():
        if fringe.isEmpty():
            return []
        node = fringe.pop()
        
        if node.getCurrentState() in explored:
            continue    # nodes should  not be added to frontier twice, 
                        # but it's too costly to check if a node is in fringe before adding
        if problem.isGoalState(node.getCurrentState()):
            return node.getActions()
        
        explored.add(node.getCurrentState())
        
        successors = problem.getSuccessors(node.getCurrentState())
        for triple in successors:
            newActions = list(node.getActions())
            newActions.append(triple[1])
            
            child = Node(triple[0], newActions, (node.getPathCost() + triple[2]))
            
            if child.getCurrentState() not in explored: # the same child can be added to frontier twice       
                fringe.push(child, child.getPathCost())
    
    
    
    """
    current_node = Node(problem.getStartState())
    fringe = util.PriorityQueue()
    count = 0
    checked = set()
    fringe.push(current_node, 0)
    while True:
        node = fringe.pop()
        if node.getCurrentState() not in checked:
            if problem.isGoalState(node.getCurrentState()):
                return node.getActions()
            successors = problem.getSuccessors(node.getCurrentState())
            checked.add(node.getCurrentState())
            count+=1
            for triple in successors:
                temp = list(node.getActions())
                temp.append(triple[1])
                child = Node(triple[0],temp, (node.getPathCost() + triple[2]))  
                fringe.push(child, child.getPathCost())
    """

def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0

def aStarSearch(problem, heuristic=nullHeuristic):
    "Search the node that has the lowest combined cost and heuristic first."
    "*** YOUR CODE HERE ***"
    current_node = Node(problem.getStartState())
    fringe = util.PriorityQueue()
    count = 0
    checked = set()
    fringe.push(current_node, 0)
    while not fringe.isEmpty():
        node = fringe.pop()
        if node.getCurrentState() not in checked:
            if problem.isGoalState(node.getCurrentState()):
                return node.getActions()
            successors = problem.getSuccessors(node.getCurrentState())
            checked.add(node.getCurrentState())
            count+=1
            for triple in successors:
                temp = list(node.getActions())
                temp.append(triple[1])
                child = Node(triple[0],temp, (node.getPathCost() + triple[2]))  
                total = child.getPathCost() + heuristic(child.getCurrentState(), problem)
                fringe.push(child, total) 
                      
                

# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
